The main motivation behind the new service is the availability of ETPs that do not track market-cap weighted indices. In particular, this includes “smart beta” (a.k.a. “strategic beta“) strategies that blend active and passive management. Due to the former aspect, smart-beta ETPs resemble traditional actively-managed mutual funds. Consequently, they can be analyzed with Alpholio™’s patented methodology, which constructs a custom reference portfolio of ETFs for each analyzed fund.

This leads to an apparent paradox: an analyzed ETP (which may be an ETF) is to be replicated by a portfolio of ETFs. Why do this at all? Just as with a traditional mutual fund, for several main reasons:

To determine whether active management aspect of the ETP adds value on a truly risk-adjusted basis

To understand the exposure of the analyzed ETP to various factors. This helps eliminate excessive exposures in the overall investment portfolio.

To replicate the ETP’s performance with other ETFs that may have preferable characteristics, such as lower fees, smaller trading premia or spreads, accessibility, etc. Conversely, to simplify a portfolio by substituting multiple ETFs with a single ETP.

To discern periods of underperformance and outperformance of the ETP after adjustment for its exposures.

…is designed to track the performance of the largest US equities, selected based on the following four fundamental measures of firm size: book value, cash flow, sales and dividends. The 1,000 equities with the highest fundamental strength are weighted by their fundamental scores.

To conduct the analysis, we will use the simplest variant of Alpholio™’s methodology, which builds a reference ETF portfolio with both fixed membership and weights. The following chart and related statistics show the cumulative RealAlpha™ for the ETP (to learn more about this and other performance measures, please visit our FAQ):

Over the five years through July 2016, the ETP added a small amount of value vs. its reference ETF portfolio of comparable volatility. The RealBeta™ of the ETF was the same as that of a broad-based equity market ETF.

The following chart with accompanying statistics presents the fixed composition of the reference ETF portfolio for the analyzed ETP:

As could be expected, due to equal-weighting of its positions this large-cap ETP had a significant tilt toward mid-cap stocks, especially of value characteristics. In addition, the ETP had considerable exposure to economic sectors such as consumer discretionary, financials, technology, and industrials.

If you would like to take advantage of the new ETP Analysis Service, please register on our website.

MarketWatch tracks eight lazy portfolios. Each of these simple portfolios consists of three to eleven, low-cost, no-load index mutual funds from Vanguard®. The fund membership and weights in each portfolio remain constant over time. (In theory, this implies that each portfolio would have to be perfectly rebalanced daily. This is not only impractical but also impossible because the fund’s daily NAVs, and hence their new weights, are not known until after the market close.)

Unfortunately, MarketWatch compares lazy portfolios solely on the basis of annualized returns in one-, three-, five- and ten-year periods. Volatility of returns as well as other performance measures are not taken into account. Luckily, Alpholio™ can help – not only does our Basic Portfolio service provide ample statistics, but it also allows for a selectable periodic rebalancing of portfolio positions to their original weights. For the purpose of this analysis, let’s assume a 15-year evaluation period from July 2001 through June 2016, as well as quarterly rebalancing of each portfolio.

Let’s start with the most complex Aronson Family Taxable Portfolio that consists of 11 funds. The following chart shows the cumulative return and related statistics for this lazy portfolio:

The alpha and beta of the portfolio were measured against the broad-based U.S. stock market ETF, and not just a large-cap index, such as the S&P 500®. Because high-yield bonds generally have a substantial correlation to equities, it could be expected that the portfolio’s beta would be approximately between 1 – (0.15 + 0.10 + 0.05) = 0.7 and 1 – (0.15 + 0.10) = 0.75, which it was at 0.73.

The key measures of risk-adjusted performance are the Sharpe and Sortino ratios. Unlike the former, the latter penalizes portfolios with a large downside deviation.

Finally, the maximum drawdown measure is the maximum percentage loss of the portfolio value from a peak to a subsequent trough. Given the chosen evaluation period, this typically means a decline in each lazy portfolio’s value from October 2007 to March 2009.

The following chart shows rolling volatility (measured as a standard deviation of two years of monthly returns) and accompanying statistics for the portfolio:

As could be expected, volatility of the portfolio significantly increased during the financial crisis. In general, a good lazy portfolio should maximize returns, minimize volatility, and reduce the magnitude of volatility changes over time.

Similar charts and statistics can easily be generated for all lazy portfolios. They are not published in this post to limit its size. Instead, here is a summary table of statistics:

Portfolio

Annualized
Return

Alpha

Beta

Sharpe
Ratio

Sortino
Ratio

Maximum
Drawdown

Aronson Family
Taxable

6.96%

0.22%

0.73

0.53

0.76

42.08%

Fundadvice
Ultimate
Buy & Hold

6.49%

0.22%

0.62

0.55

0.81

36.58%

Dr. Bernstein’s
Smart Money

6.12%

0.18%

0.65

0.51

0.75

38.00%

Coffeehouse

6.89%

0.25%

0.63

0.59

0.86

36.16%

Yale U’s
Unconventional

7.89%

0.31%

0.69

0.61

0.88

42.94%

Dr. Bernstein’s
No Brainer

6.12%

0.11%

0.83

0.43

0.63

44.48%

Margaritaville

5.86%

0.14%

0.71

0.45

0.65

41.29%

Second
Grader’s
Starter

5.92%

0.05%

0.94

0.39

0.56

49.08%

The Yale U’s Unconventional portfolio had the highest risk-adjusted returns, as measured by the above Sharpe and Sortino ratios. This was likely due to the relatively large positions in REITs and long-term government bonds, both of which benefited from falling interest rates. Please also note that at times, correlation between returns of the REIT and total stock market mutual funds was quite high (which reduced portfolio diversification), as illustrated by the following chart:

The Coffeehouse portfolio had similar characteristics. Compared to others, this portfolio also exhibited the smallest maximum drawdown.

The Fundadvice Ultimate Buy & Hold portfolio had the third best return-to-risk profile, as well as the second lowest maximum drawdown. While bond funds in this portfolio had short and intermediate maturities, its total fixed-income component was significant, as was the case with the previous two portfolios.

For completeness, here are the statistics for lazy portfolios over a ten-year period through June 2016:

Portfolio

Annualized
Return

Alpha

Beta

Sharpe
Ratio

Sortino
Ratio

Maximum
Drawdown

Aronson Family
Taxable

5.94%

0.01%

0.75

0.46

0.65

42.08%

Fundadvice
Ultimate
Buy & Hold

5.06%

0.00%

0.65

0.43

0.62

36.58%

Dr. Bernstein’s
Smart Money

5.41%

0.01%

0.67

0.46

0.66

38.00%

Coffeehouse

6.33%

0.09%

0.66

0.54

0.78

36.16%

Yale U’s
Unconventional

6.85%

0.10%

0.74

0.52

0.73

42.94%

Dr. Bernstein’s
No Brainer

5.64%

-0.06%

0.83

0.41

0.59

44.48%

Margaritaville

4.95%

-0.05%

0.73

0.39

0.54

41.29%

Second
Grader’s
Starter

5.76%

-0.10%

0.93

0.39

0.56

49.08%

Over this shorter evaluation period, the Coffeehouse portfolio had the best risk-adjusted returns, followed by the Yale U’s Unconventional portfolio, and Dr. Bernstein’s Smart Money portfolio that had a slightly higher Sortino ratio and a smaller maximum drawdown than the Aronson Family Taxable portfolio. This goes to show that the ranking of portfolios heavily depends on the analysis time frame.

We hope that this analysis will give investors additional insights into historical performance of lazy portfolios. Of course, there is no guarantee that this performance will be repeated in the future.

Alpholio™ has recently added the Dynamic Portfolio Analysis (DPA) service to its platform. The first post in this three-part series described how the DPA can be used with OFX/QFX files. The second post covered the use of DPA with Transaction CSV files. This final installment focuses on using the DPA with Return CSV files.

Let’s start with the analysis of a simple buy-and-hold equity portfolio that contained only one ETF, the SPDR® S&P 500® ETF (SPY), with all distributions reinvested. Here is how the Return CSV file for such a portfolio looks like:

As in the Transaction CSV, the first line starting with the # character is a comment that is ignored during processing of the file. The CSV has only two columns: the date of a trading day and the numerical return of the analyzed portfolio on that date. The return figure, which is typically a fraction, may also be expressed as percentage by appending the % sign. The majority of lines in this sample CSV were replaced by a single … line for visual brevity. The sample returns start on the first trading day in 2005.

The following chart with related statistics shows the cumulative RealAlpha™ for the portfolio analyzed with a regular fit:

The From date in the chart is the last trading day of the first full month of the portfolio’s lifespan. The To date is determined by the availability of historical data, through June 2016 as of this writing.

Not surprisingly, Alpholio™ determined that the portfolio had virtually no RealAlpha™; any non-zero values resulted from the limit of computational precision. The RealBeta™ of the portfolio was slightly lower than one because Alpholio™ uses a broad-based equity ETF, which includes mid- and small-cap stocks, as a proxy for the equity market.

The following chart and statistics illustrate the constant membership and weights of the reference ETF portfolio:

As could be expected, the reference ETF portfolio consisted of just one ETF (SPY), the same that constituted the entire analyzed portfolio. Please note that Alpholio™ constructed this reference portfolio only based on periodic returns, i.e. without any knowledge of the actual investment strategy, trades, positions or dollar amounts. This way, the confidentiality of investments was fully preserved.

For the second example, let’s use a diversified buy-and-hold balanced portfolio that contained multiple ETFs:

In the portfolio, each ETF had its distributions reinvested. At the end of each month, the portfolio was rebalanced to the original ETF weights. Here is how the abbreviated Return CSV looks like for the portfolio:

As in the previous example, the first line of the file is a comment, the second line is the CSV header, and subsequent lines contain trading dates and numerical returns of the portfolio.

The following chart and statistics show the cumulative RealAlpha™ for the analyzed portfolio:

The portfolio also had a negligibly small amount of RealAlpha™. Thanks to a broader asset allocation and periodic rebalancing, the portfolio’s RealBeta™ was slightly lower than the 0.6 of a traditional 60/40 portfolio.

The following chart depicts the constant composition of the reference ETF portfolio:

As anticipated, the reference ETF portfolio contained exactly the same positions and weights as the analyzed portfolio did. Again, the reference portfolio was built without any knowledge of the original individual investments. This proves the correctness and viability of this analytical approach. Of course, the DPA can evaluate any investment portfolio, not just one that solely contained ETFs.

If you would like to apply the new Dynamic Portfolio Analysis service to your investment portfolio, please register on our website.

Alpholio™ has recently added the Dynamic Portfolio Analysis (DPA) service to its platform. The previous post in this three-part series described how the DPA can be used with OFX/QFX files. This post focuses on using the DPA with Transaction CSV files.

Let’s start with the simplest input file that contains just two transactions in a hypothetical investment portfolio: a deposit of cash into the investment account and a purchase of a single mutual fund, both on the same date. Here is how this text file looks like:

The first line in the file, starting with the # character, is a comment that is ignored during processing of the CSV. The BlackRock Science & Technology Opportunities Fund (BSTSX; Service Class shares) was specifically selected for this example because it had a sufficiently long history and also did not have any distributions over the entire analysis period (which means no reinvestment of distributions was possible).

The purchase date was purposely chosen to be the last trading day of 2005. This way, the first daily return of the investment account was the first trading day in 2006, and 10.5 years of subsequent history was available.

The cash amount deposited into the account equaled the amount paid for the fund’s shares, so that the initial net cash balance was zero. The ending cash balance was also zero because the fund had no distributions. The price of the unit of cash was $1 (e.g. one share of a typical money-market fund) and the price per share of the fund was equal to the actual net asset value (NAV) of the fund on the transaction date.

The following chart depicts the cumulative RealAlpha™ and related statistics for the analyzed portfolio:

The results are identical to those of Alpholio™’s analysis of the same fund as a standalone investment, which indicates that the Transaction CSV was processed correctly. Note that the chart starts on the last trading day of January 2006 because it was the first full month of the analysis period. It ends on the last trading day in June 2016 because this was the last full month of data available as of this writing. (While the performance of the fund itself is less important in the context of this discussion, it can be noted that over the evaluation interval the fund added a modest amount of value on a risk-adjusted basis, and so did the hypothetical portfolio that contained it.)

The second example uses a hypothetical buy-and-hold portfolio with a focus on equity investments:

On the last trading day of 2005, $100,000 was deposited into the account to purchase approximately equal dollar amounts of ten stocks, each of the largest-cap public company in its respective economic sector per GICS (note that at that time, the currently separate real-estate sector was part of Financials).

The share price for each position was chosen at an approximate mid-point of the actual low and high prices on the transaction date. For simplicity, trading costs were assumed to be negligibly small. All dividends subsequently paid by the stocks were not reinvested but instead deposited as cash into the account, so that the cash position gradually increased. However, any corporate spinoffs were assumed to be immediately sold, with proceeds reinvested into the primary shares. All share splits were automatically accounted for during analysis but the portfolio was not rebalanced.

The following chart shows the cumulative RealAlpha™ and related statistics for the analyzed portfolio:

Since its inception, the portfolio added a substantial amount of value on a risk-adjusted basis. While the analyzed portfolio’s volatility, measured as the annualized standard deviation of returns, was slightly higher than that of the reference ETF portfolio, the RealBeta™ of the analyzed portfolio was significantly lower that that of the broad-based equity ETF.

The following chart and statistics show the constant composition of the reference ETF portfolio:

Alpholio™ has recently added the Dynamic Portfolio Analysis (DPA) service to its platform. Unlike the Basic Portfolio service in which the membership of the portfolio is predetermined and only subject to periodic rebalancing over the analysis period, the DPA allows for arbitrary changes in portfolio composition. This three-part post will cover the new service in more detail.

Th DPA has two main benefits: It determines whether active management of an investment portfolio has added value on a truly risk-adjusted basis. It also shows the exposure of the portfolio to various factors that may change over time.

To start the DPA, you can supply data files in one of three formats:

Open Financial Exchange (OFX) or the Quicken® proprietary version thereof (QFX). Although primarily used for interchange of banking transactions, OFX/QFX files are also available for brokerage accounts.

Transaction comma-separated values (CSV). This simple file format is proprietary to Alpholio™. The file can easily be composed from historical account records in any text editor or spreadsheet application. To improve confidentiality, stock and cash positions may be scaled by some constant factor.

Return comma-separated values (CSV). Also specific to Alpholio™, this file format is even simpler than the Transaction CSV. The file contains daily historical returns, expressed either as fractions or percentages, of the analyzed portfolio. The main advantage of this format is that, by not disclosing specific trades, positions or dollar amounts, it preserves confidentiality of the investment strategy.

To learn more about the Transaction CSV and Return CSV formats, please inquire through the Contact Us page. For security, Alpholio™ only uses all uploaded files for transient analysis and does not permanently retain them.

The following screenshot shows the upload of QFX files from a sample brokerage account that mostly contained domestic and foreign large-cap stocks paying significant dividends:

The Sweep File input is optional and used in cases where the investment account, such as at Vanguard® Brokerage Services, has a separate sub-account for a money-market fund used to automatically invest cash. The Fit Type selects the mode of the analysis (to learn more, please visit the FAQ page). Both file inputs can be reset with the Clear button.

Once the Analyze button is clicked, Alpholio™ processes both QFX files by extracting all investment and cash transactions, building portfolio values and calculating periodic returns. If there are no errors in input files, it then proceeds to analyze the portfolio just like a mutual fund. Here is the cumulative RealAlpha™ chart with related statistics for the sample portfolio:

The data files span 18 months but one month of history is ignored due to the required date alignment. The analysis indicates that the reference ETF portfolio cumulatively returned 4.8% more than the analyzed portfolio (see chart) and produced 3.3% of annualized discounted RealAlpha™ (see statistics). An investor managing this portfolio would generally be better off investing in the reference ETF portfolio instead, at least over this evaluation interval.

The volatility of both portfolios, measured as annualized standard deviation of returns, was comparable. The analyzed portfolio had a RealBeta™ lower than that of the broad-based market ETF.

The following chart and related statistics illustrate the constant composition of the reference ETF portfolio:

The next post in this series will show how the Dynamic Portfolio Analysis service can be used with a Transaction CSV file. The final post will demonstrate how to analyze a portfolio with a Return CSV file.

A recent cover story in Barron’s features liquid alternative funds from AQR. According to the article

The liquid-alt pitch is that individuals can access the same types of investments as university endowments and other big institutions, to diversify equity-heavy portfolios, typically with a 10% to 20% allocation to liquid alts… The advantage of the [AQR Managed Futures] strategy […] is that it is uncorrelated with other asset classes, and “has the most consistently strong performance in equity bear markets.” That is when diversification matters most, as was the case in the third quarter of last year and the early part of this year.

Ideally, returns of a liquid-alt fund should not only be uncorrelated with those of both stocks and bonds but also significantly positive over a long evaluation period. Let’s take a look at the performance of three AQR funds with a sufficiently long history.

Please note that AQMIX had the first full month of returns in February 2010. Consequently, the first rolling 36-month return became available at the end of January 2013. As could be expected, the fund had lower correlation to stocks than to fixed income, although both coefficients were quite low (generally, correlation below 0.6 provides diversification benefits).

In contrast to AQMIX and ASAIX, this strategy had a higher correlation to equities than bonds; however, both coefficients were still pretty low.

The problem with any of these strategies is the lack of accessibility for most individual investors:

AQR’s approach can be hard to understand. Because of this—and to deter hot money—the firm sells its liquid-alt funds almost entirely through financial advisors. Retail buyers can access the funds directly through fund supermarkets like Fidelity, but direct investments involve a minimum of $1 million. Investments through advisors and 401(k) plans have no minimum.

Is there a way to substitute these liquid-alt funds with readily available ETFs? Let’s explore this possibility using Alpholio™’s patent-based analysis service for mutual funds. One variant of this methodology constructs a reference portfolio of ETFs with fixed both membership and weights. Here is the resulting cumulative RealAlpha™ chart for the AQR Managed Futures Strategy Fund (to learn more about this and other performance measures, please visit our FAQ):

As the statistics section below the chart shows, since its inception the fund had a smaller return and a much higher volatility (measured by standard deviation) than those of the reference portfolio. The following chart illustrates the constant composition of the reference ETF portfolio in this analysis:

The return correlation of the reference ETF portfolio over the entire evaluation period was 0.16 with VTI and 0.58 with BND. Given that these figures for AQMIX were approximately -0.07 and 0.21, respectively, the reference portfolio was not as good a diversifier for stocks and bonds as the fund was. However, the reference portfolio only had long positions in non-leveraged ETFs. It also returned about 8% more than the fund on a cumulative basis and with a 59% lower volatility. Similar analyses can be conducted for ASAIX and ADAIX. In the end, it is up to the investor to weigh the pros and cons of using reference ETF portfolios as substitutes for these funds in the context of the overall portfolio.

We hope that our Investment Toolkit™ will provide useful services for investors who want to construct well-diversified portfolios. If you would like to use it, please register on our website.

A recent column from Bloomberg Gadfly discusses increasing correlations of asset classes. The correlation coefficient of periodic returns is a measure of the extent to which these returns move in the same direction. Contrary to a common misconception, a high correlation does not imply that the two assets or classes are identical. Ideally, to diversify a portfolio, long-term correlations among portfolio components should low.

This correlation shift has a major impact on portfolio construction following the Modern Portfolio Theory. The article uses ten-trailing-year correlations of various indices:

While a point-in-time analysis of ten-year correlations between indices is instructive, it is of little practical value to investors. Luckily, Alpholio™ has just introduced a new Multi-Correlation service, which provides an interactive analysis of rolling correlations.

In a typical analysis, monthly returns are used because they are less “noisy” than weekly or daily returns. A span of 36 months (three years) of returns is usually sufficient to approximate the long-term correlation and, at the same time, to nimbly react to rapid correlation changes. A rolling-period approach provides insights on how the correlation coefficient evolved over time. It can also facilitate calculation of useful statistics. Finally, instead of artificial indices that cannot be bought or sold, Alpholio™ uses ETFs.

To demonstrate the Multi-Correlation service in action, here is a chart of rolling correlations between each of several analyzed ETFs and one reference ETF:

Please note that the youngest of these ETFs (GSG) determines the common time frame of this analysis: the first full month of GSG returns was August 2006, so the first 36-month rolling return became available at the end of July 2009. The Multi-Correlation service determines the longest possible analysis interval automatically.

The following table contains statics of rolling correlations between each analyzed ETF and the reference ETF:

By default, the statistics are ordered in the ascending order of median value, but can be reordered in any ascending/descending order by clicking on the header of the respective column. In conjunction with the chart, these statistics show that each of the equity ETFs had a substantial (above 0.6) correlation to commodities, while the REIT ETF had the lowest correlation. The correlation of all four analyzed ETFs to the reference ETF declined from the second half of 2014 onward, with the REIT ETF showing the strongest decoupling. The Forecast statistic is the expected value of the rolling correlation one month forward, or in February 2016 in this example.

We hope that the Multi-Correlation service will become a useful tool for investors who want to construct well-diversified portfolios. If you would like to use this and other Alpholio™ services, please register on our website.

The popular market-proxy S&P 500® index is market-cap weighted. This is one of the factors that helps reduce the turnover of ETFs tracking this index. For example, the iShares Core S&P 500 ETF (IVV) has a turnover rate of only 4%. The following chart, produced by the Alpholio™ App for Android, shows the characteristics of a portfolio composed solely of this ETF:

(Note that Alpholio™ uses a broader ETF as a representation of “the market”; hence, the beta of IVV is different from the conventional one and alpha from zero.)

However, market-cap weighting implies that the largest companies’ stocks have the highest impact on the index. While returns of mega-caps in the index tend to be less volatile, they are usually lower than those of their smaller-cap peers. To overcome this limitation, other ETFs weight equities in the index differently. For example, the Guggenheim S&P 500™ Equal Weight ETF (RSP) assigns each of the 500 stocks a 0.2% weight. This tilts RSP toward smaller-cap equities in the index and results in a 18% turnover. Over the same analysis period, RSP produced markedly higher returns than IVV but at the expense of an elevated volatility and a slightly lower Sharpe ratio:

In addition to overweighting of mega-caps, some economic sectors in the index dominate others, as shown in the latest edition of S&P Capital IQ The Outlook:

Sector

Weight %

Consumer Discretionary

12.7

Consumer Staples

9.4

Energy

7.8

Financials

16.5

Health Care

15.3

Industrials

10.2

Information Technology

19.9

Materials

3.2

Telecommunication Services

2.2

Utilities

2.9

To counteract this, the ALPS Equal Sector Weight ETF (EQL) applies the same weight to nine sectors (with telecommunication services considered part of information technology). Here are the characteristics of a portfolio consisting solely of this ETF over the identical analysis period:

While the annualized return of EQL was lower than than of IVV or RSP, it was more than adequately offset by a decrease in volatility, which resulted in an improved Sharpe ratio and maximum drawdown.

What if the investor wanted to equal-weight all ten sectors instead of just nine, i.e. keep telecoms separate from IT? To do so, the investor could construct a portfolio of Vanguard sector ETFs, excluding the Vanguard REIT ETF (VNQ). That is because real estate stocks are currently part of the financials sector and not expected to become a separate asset class until mid-2016. Here is how such a portfolio, rebalanced quarterly (just like EQL), performed over the same analysis period:

The Vanguard sector portfolio had the second highest alpha and Sharpe ratio as well as the second lowest standard deviation (a measure of volatility of returns).

The above analysis period was dictated by the inception date of the EQL, the youngest of all the ETFs used. Arguably, this approximately six-year period may be considered too short and not representative of performance over a full economic cycle. However, it was interesting to see that while equal-weighting the index on a security level produced highest absolute returns, equal-weighting on a sector-level delivered the highest risk-adjusted returns.

To conduct your own analyses of various ETF portfolios, download the Alpholio™ app from

In a traditional portfolio, mid-cap and small-cap equities receive much smaller weights than large-caps. For example, the most recent moderate asset allocation model portfolio recommended by the S&P Capital IQ Investment Policy Committee (see in the November 24, 2014 edition of the S&P The Outlook), consists of the following allocations:

50% to U.S. equities

15% to foreign equities

25% to bonds

10% to cash

To achieve the model allocation, the committee recommends specific ETFs for the 50% U.S. equity part of the portfolio:

Therefore, the mid-cap and small-cap stocks collectively account for only 20% of domestic equities in the portfolio. Is such a low allocation justified by historical performance of these asset classes? Let’s take a look using the Portfolio Service of the Alpholio™ App for Android.

The longest analysis time frame is determined by the existence of IJR, whose first full monthly return was in June 2000 (SPY’s first monthly return was in February 1993, and MDY’s in June 1995). Here are the statistics of a portfolio solely composed of SPY in a period from that month through 2014:

Similarly, for MDY:

And for IJR:

The mid-cap (MDY) and small-cap (IJR) ETFs had annualized returns more than twice that of the large-cap ETF (SPY). The Sharpe ratios of MDY and IJR were also approximately twice that of SPY. While IJR outperformed MDY in terms of the annualized return, alpha and Sharpe ratio (just slightly), it also had the highest standard deviation (volatility), maximum drawdown and beta of all three ETFs. Therefore, the mid-cap ETF appears to be a decent compromise between risk and reward.

In the evaluation period, MDY clearly outperformed its peers by generating the highest annualized return, alpha and Sharpe ratio, while having the lowest maximum drawdown.

Another service offered by the Alpholio™ App for Android is the Rolling Returns analysis. In the 10-year period through 2014, SPY returned more than VTI in about 9.4% of all rolling 36-month periods (a rolling period of 36 months aims to approximate the average holding time of the ETF in an investment portfolio):

However, in the same period, MDY outperformed VTI in about 75.3% and IJR in 70.6% of all rolling 36-month periods. Based on this simple measure (it does not take risk into account), MDY again demonstrated a superior performance.

While past performance is not a guarantee of future results, this analysis indicates that mid-cap equities may deserve a higher allocation even in a moderate-risk portfolio. A follow-on post will examine the characteristics of growth vs. value equities, also using services of the Alpholio™ App for Android. The app is available at:

Today’s post on Yahoo Finance discusses an “all weather” portfolio recommended by one of the most famous hedge fund managers. The portfolio strives to achieve an equal distribution of risk across macro periods of inflation, deflation, high and low economic growth.

The portfolio consists of:

30% stocks

15% intermediate-term government bonds

40% long-term bonds

7.5% gold

7.5% commodities

The portfolio has a large fixed-income component relative to equities to get close to a risk parity (yet, it does not use bond derivatives). The portfolio should be rebalanced at least annually.

These ETFs were selected to have the earliest possible inception dates and lowest sponsor fees (expense ratios). The time span of the analysis is limited by the inception date of DBC. An alternative commodity ETF, the iShares S&P GSCI Commodity-Indexed Trust (GSG), became available about five months after DBC, therefore the latter was chosen. Since about 8% of DBC tracks gold, the weight of IAU is lower than that of DBC by one percentage point (due to the limitation of setting widgets, the app only accepts whole percentage weights).

Here is the setup for the analysis (the Dates, Return Frequency and Rebalance Frequency sections can be expanded by tapping their respective headers):

Here are the analysis results for the above portfolio with monthly returns and quarterly rebalancing:

With semi-annual (i.e. every six months) rebalancing, the all weather portfolio performed slightly better in terms of the higher annualized return and Sharpe ratio as well as smaller maximum drawdown:

Annual rebalancing yielded no further improvement in the annualized return or Sharpe ratio, but reduced the maximum drawdown to 12.1% and lowered the beta to 0.20.

For reference, here are the results for a traditional balanced portfolio, comprised of 60% SPY and 40% of iShares Core U.S. Aggregate Bond ETF (AGG), with monthly returns and semi-annual rebalancing in the same analysis period:

Compared to the traditional balanced portfolio, the all weather portfolio had all the desirable characteristics: a higher annualized return and Sharpe ratio, coupled with a significantly lower beta and maximum drawdown. However, the above analysis covered a prolonged period of decreasing and historically low interest rates that drove the returns of intermediate- and long-term bonds, the dominant positions in the portfolio. In an environment of rising interest rates (generally expected to begin next year) and falling commodity prices (already taking place), a risk-parity oriented portfolio, even with no bond leverage, may suffer.